Multi-level context feature fusion for semantic segmentation of ALS point cloud

Semantic segmentation of airborne laser scanning (ALS) point clouds using deep learning is a hot research in remote sensing and photogrammetry. A current trend is to aggregate contextual features from different scales for boosting network generalization and diversity discrimination capabilities. One main challenge is how to achieve effective fusion with multiscale information. In this letter, we propose a muti-level context feature fusion network (MCFN) for semantic segmentation of ALS point cloud based on an encoder-decoder structure. More specifically, we design the squeeze-expansion shared MLP module (SE-MLP) following kernel point convolution (KPConv) in the encoding stage, which can extend the receptive field of KPConv. To aggregate low-level features and high-level representations, we establish channel self-attention between skip connections. In the decoding stage, we develop a cross-layer attention fusion module (CAF) to generate additional discriminative channel features by fusing multi-scale features at different upsampling layers. Experiments on the ISPRS and LASDU datasets demonstrate the superiority of the proposed method. Code: https://github.com/SC-shendazt/MCFN.